Dual-Alpha: a large EEG study for dual-frequency SSVEP brain–computer interface

Abstract Background The domain of brain–computer interface (BCI) technology has experienced significant expansion in recent years. However, the field continues to face a pivotal challenge due to the dearth of high-quality datasets. This lack of robust datasets serves as a bottleneck, constraining the progression of algorithmic innovations and, by extension, the maturation of the BCI field. Findings This study details the acquisition and compilation of electroencephalogram data across 3 distinct dual-frequency steady-state visual evoked potential (SSVEP) paradigms, encompassing over 100 participants. Each experimental condition featured 40 individual targets with 5 repetitions per target, culminating in a comprehensive dataset consisting of 21,000 trials of dual-frequency SSVEP recordings. We performed an exhaustive validation of the dataset through signal-to-noise ratio analyses and task-related component analysis, thereby substantiating its reliability and effectiveness for classification tasks. Conclusions The extensive dataset presented is set to be a catalyst for the accelerated development of BCI technologies. Its significance extends beyond the BCI sphere and holds considerable promise for propelling research in psychology and neuroscience. The dataset is particularly invaluable for discerning the complex dynamics of binocular visual resource distribution.


Data Description
Brain-computer interface (BCI) research is currently one of the most vibrant fields of study [ 1 , 2 ].Among v arious BCI tec hnologies, electr oencephalogr am (EEG)-based interfaces are deemed particularly suitable for consumer electronics applications in sectors like education due to their noninv asiv e natur e and ease of use [ 3 , 4 ].Within this domain, steady-state visual evoked potential (SSVEP)-based BCIs hav e emer ged as some of the most accurate and stable systems available [ 5 , 6 ].
SSVEPs ar e fr equency-loc ked and phase-loc ked br ain activities pr edominantl y occurring in the occipital region when an individual observes a flickering light stimulus at a fixed frequency [ 7 ].These signals are extensively utilized in BCI research for functions such as typing and de vice contr ol.Giv en that SSVEP r e-sponses are typically confined to specific frequency bands [ 8 , 9 ], dual-frequency SSVEP studies have become a focal point, aiming to enhance the capacity of SSVEP systems to handle more extensiv e tar get selections [ 10 ].The explor ation of dual-fr equency SSVEP r epr esents one of the most pr omising ar eas of current research.
Recent years have seen the proposal of v arious dual-fr equency stim ulation tec hniques by r esearc hers, encompassing methods like the c hec kerboard arr angement (CA) par adigm [ 10 ] and the left-right visual field paradigm, among others.A notable advancement is the enhanced CA introduced in 2020 [ 11 ].Ho w ever, a persistent challenge across these paradigms is the generation of unpredictable intermodulation harmonic components (UIHCs) in the form a * f 1 + b * f 2 , where a and b are arbitrary integers [10][11][12][13].Attempts to harness these intermodulation frequencies for coding hav e lar gel y been unsuccessful due to their instability and individual variability [ 12 , 13 ].
In response, a study in 2022 introduced a dual-frequency SSVEP paradigm named binocular vision (BV) using 3-dimensional (3D) display tec hnology, le v er a ging polarized light to effectiv el y separate the dual frequencies and reduce UIHC generation [ 14 ].Furthering this a ppr oac h, the 2024 intr oduction of the binocular-swa p vision (BsV) paradigm utilizes a similar stim ulation str ategy but incor por ates a specialized coding and decoding algorithm to efficiently utilize the differential visual capacities of the 2 eyes, making it one of the most effective dual-frequency SSVEP BCI systems to date [ 15 ].Both the BV and BsV paradigms employ identical stimulus and data acquisition methods; ho w ever, they differ significantly in their coding schemes .T he BV paradigm continues to r el y on tr aditional fr equency identification for decoding, wher eas the BsV par adigm, facing the pr esence of tar gets with identical frequencies, places greater emphasis on the differences in the spatial distribution of dominant eye effects for decoding pur poses.Consequentl y, the BsV par adigm exhibits enhanced potential for coding and decoding within dual-frequency paradigms.
The pr ogr ession of algorithmic r esearc h in BCIs is incr easingly leaning to w ar d data-driv en a ppr oac hes, underscoring the critical need for high-quality datasets [ 16 ].There is a plethora of SSVEP datasets covering diverse aspects, including real-world usage scenarios [ 17 ], motion-based datasets [ 18 ], and m ultifr equency SSVEP datasets [ 19 ], along with mixed-paradigm datasets [ 20 ].
Ho w e v er, high-quality datasets specificall y cr afted for the pr e v alent 40-tar get SSVEP input k e yboards ar e notabl y scarce.This is particularly critical given that one of the primary applications of SSVEP technology is curr entl y the de v elopment of these 40-target k e yboards [ 21 , 22 ].Despite this, the field still faces a significant shortage of comprehensive dual-frequency 40-target SSVEP datasets, whic h ar e essential for the adv ancement of BCI technologies.To bridge this gap, we have developed the Dual-Alpha dataset.This dataset is uniquely designed for the 3 most effectiv e dual-band par adigms-CA, BV, and BsV-and is distinguished as the largest and only dual-frequency SSVEP dataset tailor ed specificall y for 40-tar get a pplications.

Participant information and experimental setup
Our study included over 100 participants.Detailed demogr a phic information is presented in Table 1 .The experiments for the 3 par adigms wer e conducted independentl y, with voluntary en-r ollment, and eac h subject was number ed in the order of enrollment.For the CA paradigm, 35 individuals participated, with an av er a ge a ge of 23.9 years, comprising 22 males and 13 females.In the BsV paradigm, 35 participants were involved, with a mean age of 23.3 years, including 21 males and 14 females.Similarly, in the BV paradigm, 35 participants were involved, with a mean age of 23.2 years, including 23 males and 12 females.Notably, the majority of participants were unfamiliar with SSVEPbased BCI technologies.None of the participants had any ophthalmic or neurological conditions.Some subjects participated in m ultiple par adigm experiments; this information is pr ovided in Supplementary Table S1 .
As illustrated in Fig. 1 , each participant was seated in a dark, electr oma gneticall y shielded r oom, maintaining a fixed distance of 80 cm from the stimulus screen.The trial commenced with a 1-second cue period, during which the target for the next stimulus was highlighted in red, allowing the participant to focus .T his w as follo w ed b y a 2-second stim ulation period, wher ein the participants concentr ated solel y on the pr e viousl y cued tar get.A subsequent 1-second rest period was observed, during which participants were advised to remain still and avoid any movements or blinking.The stimulus and signal acquisition methods for CV is shown in Fig. 1 (I).And The stimulus and signal acquisition methods for both the BV and BsV par adigms wer e identical, thus the dia gr ams of these two par adigms ar e pr esented in Fig. 1 (II).Participants wore polarizing glasses throughout the experiments.For subjects who wore glasses, clip-on polarized glasses were used, and for those who did not wear glasses, frame polarized glasses wer e pr ovided.Eac h participant underwent a total of 200 trials, with each of the 40 targets being presented in 5 distinct trials.The sequence of stimulus targets was randomized by the computer system to pr e v ent anticipatory biases.
The configuration of the stimulus targets is depicted in Fig. 2 .The luminance sequences for all the targets were designed based on the joint frequency-phase modulation (JFPM) technique [ 24 ].In the dual-frequency stimulus configuration, involving frequencies f 1 and f 2 , the luminance sequences ar e mathematicall y expressed as: where S denotes the luminance sequence of each frame, with values r epr esenting the gr ay le v els on the display r anging fr om 0 to 255.The variable σ represents the number of frames, with the display r efr esh r ate being 60 Hz , and hence σ v aries fr om 1 to 60 multiplied by the stim ulation dur ation.ϕ denotes the phase, and f r epr esents the stim ulation fr equency.r indicates the display r efr esh r ate.
Regarding the spatial configuration of the stim ulus tar gets, 3 par adigms ar e addr essed in this study.For the CA paradigm, the stim ulus tar get is illustr ated on the left side of Fig. 2 (I) and is structur ed similarl y to a chessboard grid.For the 2 frequencies   f 1 and f 2 , they are alternated among the stimulus targets, with each small grid measuring 3 pixels, totaling a stimulus target size of 132 × 132 pixels.To human perception, the stimulus target appears as a combination of 2 distinct frequencies .T he stimulus tar gets wer e arr anged at equal interv als acr oss the scr een in the form of 5 rows and 8 columns, and the luminance of the interval portion was alwa ys 0. T he specific arrangement can be seen in Fig. 3 .
The BV paradigm and the BsV paradigm constructions are presented on the right side of Fig. 2 (I).To the human eye, the stimulus appears as a summation of f 1 and f 2 frequencies.Ho w ever, upon closer inspection, the stimuli are interlaced, with only 1 pixel per line, making the spatial differences imperceptible to the human eye .T he demodulation processes for BV and BsV are illustrated in Fig. 2 (II), wher e the vibr ational phases of the polarized light emitted by f 1 and f 2 stimuli differ.These can be remodulated through the demodulation of polarizing glasses to f 1 and f 2 , with f 1 presented to the left eye and f 2 to the right eye.It is worth noting that the polarized light technique causes each eye to see only half of the pixels on the stimulus screen emitting light, the other half being filtered out by the polarizer due to a phase mismatch.T hus , the intensity of light for stimuli in the BV and BsV paradigms is actuall y onl y half that of the CV par adigm.

Stimulus interface and encoding
Figure 3 illustrates the stimulus configuration and encoding methodologies emplo y ed in this resear ch.The coding of CV and BV paradigms adopts the optimal coding scheme from the study by Liang et al. in 2020 [ 11 ].The BsV paradigm utilizes the coding sc heme deriv ed using the global optimization method from the study by Sun et al. in 2022 [ 14 ].Among the 3 paradigms examined, the CV and BV par adigms adher e to tr aditional fr equencyidentification coding schemes .T hese schemes necessitate unique frequency combinations for each target, ensuring that the responses ev oked b y eac h tar get differ significantl y in the fr equency spectrum.The coding scheme utilized in this study represents the optimal combination of frequencies, as proposed in prior research.The specific arrangement of frequencies and phases, along with their values, is detailed in Fig. 3 (I).
Conv ersel y, the BsV par adigm le v er a ges the spatial differences arising from the prevalent dominant eye effect in the population to facilitate classification.Within this paradigm, some stimulus tar gets shar e identical fr equency combinations, r esulting in nearl y indistinguishable e voked r esponse spectr a. Ho w e v er, the assignment of frequencies to the left and right eyes differs (e.g., for stim ulus tar get A: left eye f 1 , right eye f 2 ; for stim ulus tar get sw ap-A: left ey e f 2 , right ey e f 1 ), whic h cr eates distinct spatial patterns in the responses that are used for classification tasks.The encoding scheme for the BsV is depicted in Fig. 3 (II), where targets enclosed in solid borders represent the top 20-group encoding sc heme deriv ed fr om global optimization in pr e vious studies.Targets within dashed borders indicate ne w tar get gr oups r esulting fr om a swa p of stim ulus fr equencies between the left and right eyes.

Data acquisition and processing
For the data acquisition in this study, a NEUROSCAN EEG amplifier ( RRID:SCR _ 015818 ) and a 64-lead Neuroscan Quik-Cap EEG Cap ( RRID:SCR _ 015817 ) w ere emplo y ed, adhering to the international 10-20 system for electrode placement.In the case of the CA and BV par adigms, whic h primaril y involv e the occipital r egion, onl y the 9 electrodes located in this area were utilized, specifically Pz, PO5, PO3, POz, PO4, PO6, O1, Oz, and O2.For the BsV paradigm, owing to the broader distribution of significant interclass differences across the brain regions [ 15 ], data from all 64 electrodes were collected.
The acquired experimental data underwent a downsampling pr ocess to decr ease the sampling r ate fr om 1,000 Hz to 250 Hz.This was followed by the application of comb filters to eliminate direct current signals and reduce intermediate frequency interference, utilizing the MNE toolbox ( RRID:SCR _ 005972 ) [ 25 , 26 ].The data pr epr ocessing was carried out from raw data using the EEGLAB ( RRID:SCR _ 007292 ) toolkits, known for their computational efficiency [ 27 ].
The assessment of the signal-to-noise ratio (SNR) was performed to better e v aluate the performance of the dual-band paradigm.The calculations for wideband SNR, narrowband SNR, and intermodulation SNR [ 14 ] were conducted as per the following formulas: In the given study, the variables SN R Broadband , SN R Narrowband , and SN R Intermodulation denote the values of the wideband SNR, narrowband SNR, and intermodulation SNR, r espectiv el y.The terms f 1 and f 2 correspond to the combination of stimulus frequencies utilized in the dual-band configuration.The function N indicates the energy associated with these frequency points .T he symbol h signifies the number of harmonics consider ed, whic h, for this r esearch, is set at 5. The parameter f b , defined as 2 in this stud y, re presents the bandwidth utilized for the narrowband evaluations.
f is the r ecipr ocal of the sampling time length of the signal, which is 1 / 2 in this study.
The wideband SNR quantifies the ratio of the energies of f 1 and f 2 , along with their harmonics, r elativ e to the entir e fr equency spectrum, thus reflecting the strength of the SSVEP signal.The narro wband SNR, piv otal for SSVEP classification accurac y, is calculated as the ratio of the energy of f 1 and f 2 , including their harmonics, to the energy within a 4 Hz bandwidth centered on these frequencies.
Furthermor e, the intermodulation SNR, whic h is crucial for assessing the strength of the UIHC specific to dual-band stimuli, is measured as the ratio of the energies of f 1 and f 2 , and their harmonics, to the energy at the frequency band where UIHC ( a f1 + b f2 , where a , b r ange fr om −5 to 5) is observed.It is noteworthy that higher values of intermodulation SNR correspond to weak er re presentations of the UIHC, which implicates its diminished influence in the presence of strong intermodulation components.

SSVEP classification algorithm
To e v aluate the quality of the dataset further, classification analysis was performed using established algorithms within the domain.The SSVEP classification algorithms fall into 2 primary categories: nontraining and training-based methods [ 28 ].Ho w ever, due to the limited adaptation of many algorithms to the dualfr equency par adigm, we selected 1 r epr esentativ e algorithm fr om each category for our analysis.
For the nontraining category, we utilized the filter bank dualfr equency canonical corr elation anal ysis (FBDCC A) [ 14 ].T his method is an adaptation of the classical filter bank canonical  corr elation anal ysis (FBCCA) [ 29 ], specificall y modified to handle dual-frequency SSVEP systems .T he FBDCC A algorithm enhances the detection of dual-fr equency tar gets by modifying the templates of FBCCA to accommodate dual frequencies.In our study, we constructed templates using sine-cosine matrices deriv ed fr om the first to fifth harmonics of the 2 fr equencies associated with the stimulus targets .T hese templates were then processed through a filter bank composed of 5 bandpass filters, with ranges set at [5,  was performed, and the outputs were linearly weighted to generate the final correlation sequence .T he template displa ying the highest correlation was identified as the predicted result.
In the training-based category, we employed the task-related component analysis (TRCA) algorithm [ 30 ]. TRCA enhances classification performance by using training data to compute a nulldomain filter, thus optimizing the detection of task-related components.In this r esearc h, the v alidation was conducted using the leave-one-out method, and the aggregate results were expressed as mean values .T he filter banks were configured with the follow- Additionally, due to the constraints in time length for plotting traditional spectra, we opted to use CCA spectra instead.This a ppr oac h utilizes the corr elation v alues calculated by the CCA algorithm [ 31 ], denoted as ρ, plotted a gainst fr equency, pr oviding a spectrum-like r epr esentation but with higher resolution [ 32 ].The method first constructs the desired sine-cosine template and subsequently performs a CCA operation with the cor-responding EEG data time series to obtain corr elation v alues.Although this method does not provide phase information, its frequency resolution is higher.We consider that this method sacrifices phase information to enhance frequency resolution.The template Temple ( f, t ) can be r epr esented by the following equation:  The computation of this spectrum is described by the following equation: Her e, ρ r epr esents the v alue on the v ertical axis of the CCA spectrum, and f denotes the fr equency, r anging fr om 5 to 35 Hz with increments of 0 . 1 Hz in our analysis .T he variable t represents the time-series data, and x (t) r epr esents EEG data time series.
In addition, we use the ITR metric in measuring the classification accuracy of the SSVEP system, which is calculated as in Equation 4, where T is the length of the selected time window (in seconds), and an additional 0.5 seconds will be used as the target search time to simulate the real situation.n is the number of stimulus targets.p is the classification accuracy, with a value between 0 and 1.

Frequency domain analysis validation
To ascertain the integrity of the dataset, we initially engaged in the analysis of time-domain signals and distributions, presenting r epr esentativ e r esults in Fig. 4 .As depicted in Fig. 4 (I), the UIHC in the CA paradigm exhibits significant strength, and there is consider able v ariability both within and between subjects r egar ding the ev oked frequencies .For instance , the subject illustr ated in Fig. 4 (I) demonstr ated a UIHC at a frequency of 11 .6 Hz , calculated as 6 * f 1 − 4 * f 2 .Conv ersel y, the primary frequen-  T he green asterisks are the results of W elch' s independent t -tests for significant differences between the BV paradigm and the BsV paradigm, and the dark red asterisks are the results of W elch' s independent t -tests for significant differences between the CA paradigm and the BsV paradigm.There was no significant difference between the results of the CA and BV paradigms.
cies of the CA par adigm, specificall y components f 1 and f 2 , displayed instability; for example, the 10 .6 Hz stimulus in Fig. 4 (I) was nearly imperceptible, yet its second harmonic at 21 .2 Hz was pronounced.
The UIHC in the BV paradigm was comparatively less prevalent, and its main frequency component appeared more stable, as evidenced in Fig. 4 (II).This stability can be attributed to the application of polarized light tec hnology, whic h effectiv el y pr e v ents the ov erla p of the 2 stimulus frequencies before reaching the retina.Nonetheless, the BV paradigm did not eliminate the occurrence of UIHC, as demonstrated by the presence of a 19 .6 Hz frequency ( f 1 + f 2 ) in Fig. 4 (II).These findings align with pr e vious r esearc h [ 14 ], underscoring the high quality of the dataset.
For the BsV par adigm, e v aluations wer e conducted independently due to its distinct encoding a ppr oac h and the acquisition of a more extensive array of leads.Figure 5 illustrates typical frequency domain and topogr a phic ma p sc hematics; Fig. 5 (I) displays the left eye stimulus analysis results at frequency f 1 of 10 .9 Hz and the right eye at frequency f 2 of 13 .93 Hz , while Fig. 5 (II) pr esents the inv erse.These r esults highlight that the fr equency c har acteristics e v oked b y these stim ulus tar gets ar e r emarkabl y similar and nearly identical.Ho w ever, there is a notable difference in their PSD topogr a phy, attributed to the disparate allocation of visual resources between the 2 eyes .T his differ ential r esource distribution underscores the efficacy of the BsV paradigm in performing classifications.

SNR r a tio distribution anal ysis
To assess the ov er all quality of the dataset, we computed the wideband SNR, narrowband SNR, and intermodulation SNR for a single trial across each of the 3 paradigms, with the results depicted in Fig. 6 .When compared to datasets such as Beta [ 33 ], our SNR distributions are all normal but ov er all mor e ske wed.This ske wness corr elates with the pr esence of UIHC in the dualfr equency par adigm, among other factors.Compar ed to the same m ultifr equency dataset study [ 19 ], our SNR distributions are very similar.These findings confirm the stability and quality of our dataset.Notably, the distribution of the BsV paradigm in the intermodulation signal-to-noise ratio exhibited a significant shift.This shift is thought to be associated with the distribution of the dominant eye among the subject population, pr edominantl y righteyed, as detailed in Table 1 .This factor likely influenced the generation of the UIHC, underscoring the dataset's considerable potential for psychological and neurobiological research.Noting that although the BsV paradigm was acquired for 64 leads at the time of acquisition, only data from the 9 leads of the occipital region were used in the calculation of SNR as in the other 2 paradigms.

Average SNR
Further anal ysis involv ed calculating the av er a ge SNR, with findings presented in Fig. 7 .The BsV paradigm exhibited relatively high values for both wideband and narrowband SNR, followed by the BV paradigm, while the CA paradigm recorded the lowest v alues, likel y due to the instability of the dominant fr equency in this paradigm.In terms of intermodulation SNR, both the BV and BsV paradigms outperformed the CA paradigm, suggesting a lo w er generation of UIHC in these paradigms .T hese results align with pr e vious r esearc h, affirming the dataset's quality [ 14 ].Howe v er, it is important to note that both the wideband and narrowband SNR of the current dataset are lo w er than those reported in single-fr equency SSVEP datasets, potentiall y due to the div ersion of UIHC for total stimulus response energy.

Classification results without training
Given that the SSVEP paradigm predominantly serves classification tasks, we analyzed the dataset accordingly.For the notraining scenario, we implemented the FBDCCA method.Due to the inher ent c har acteristics of the BsV par adigm, whic h encodes the same for 2 sets of targets, it precludes the feasibility of notr aining classification.Ther efor e, our anal ysis was confined to the CA and BV paradigms, with the findings depicted in Fig. 8 .It is evident from the figure that the BV paradigm, benefiting from a stable principal fr equency, r etains some utility e v en without training.In contrast, the CV paradigm proves virtually inapplicable without training due to significant individual variability in the UIHC.Specific categorization results can be found in Supplementary Table S2 .

Classification results with training
Subsequently, we conducted an algorithmic analysis incorporating training, employing the TRCA algorithm within the SSVEP fr ame work.This computation was executed using the leave-oneout a ppr oac h, utilizing 4 trials for training and 1 for testing at each instance .T he a v er a ge outcomes and the results of statistical tests ar e illustr ated in Fig. 9 .The r esults indicate that the performance metrics of correctness and ITR for both the CA and BV paradigms ar e closel y matc hed, with no significant differ ence observ ed.The CA par adigm slightl y outperformed, possibl y due to the polarized light technique used in both the BV and BsV par adigms, whic h reduces light intensity by half.Despite the BsV's close frequency resemblance and its focus primarily on the null domain, it does not match the efficacy of TRC A algorithms .T he BsV paradigm is trained and tested with 64-lead data.While ther e ar e specialized algorithms enhancing performance in the null domain [ 15 ], they do not a ppl y to the other paradigms and thus are not discussed in this article.Specific categorization results can be found in Supplementary Table S3 .

Effect of the number of channels on classification
Lead selection is a critical factor in BCI studies focusing on SSVEP [ 34 ].This study emplo y ed a 9-channel acquisition system targeting the occipital region, based on findings from a previous singlefrequency study.To determine whether the 9-channel configuration pr ovides nonr edundant information acr oss the 3 par adigms inv estigated, classification tasks wer e conducted sequentiall y using 3-channel (O1, Oz, and O2), 6-channel (PO3, POz, PO4, O1, Oz, and O2), and 9-channel (Pz, PO5, PO3, POz, PO4, PO6, O1, Oz, and O2) setups, utilizing the TRCA algorithm.The results, depicted in Fig. 10 , indicate that the classification accuracy for all 3 paradigms impr ov es with an increasing number of channels.

Conclusion and Discussion
In this study, we introduced the Dual-Alpha dataset, the largest and onl y dual-fr equency SSVEP dataset specificall y tailor ed for 40-tar get a pplications.Our compr ehensiv e dataset, encompassing over 100 participants, underwent rigorous validation through SNR analyses and classification.The validation results highlighted the dataset's high quality and stability.
Despite these strengths, our dataset has certain limitations.One significant issue is the sync hr onous natur e of the data, whic h r estricts its a pplicability to specific types of BCI systems .T he curr ent dataset lac ks async hr onous system data, whic h is crucial for de v eloping mor e flexible and pr actical BCI a pplications [35][36][37].Async hr onous systems allow for more natural and spontaneous inter actions, better mimic king r eal-life scenarios wher e users can switch tasks and modes of operation without predefined time constraints.
Futur e r esearc h should focus on expanding the dataset to include async hr onous system data.This would enable the de v elopment of more sophisticated algorithms capable of handling the dynamic and unpredictable nature of real-world BCI applications.Additionall y, inv estigating methods to further reduce the presence of UIHC and enhance the robustness of frequency-locked responses acr oss differ ent par adigms will be essential for advancing r esearc h of dual-frequency SSVEP BCI.

Usage Notes
The dataset is organized into 3 folders, each corresponding to a differ ent par adigm: Binocular Vision/ Binocular-Swap Vision/ Checkerboar d Arr angement/ Each folder contains data files for individual subjects, named in the format "SUBJECT X.csv."The structure of these CSV files is as follows: Each CSV file contains EEG data from 5 blocks , co vering 40 targets, resulting in a total of 200 trials per subject.The data are sampled at 250 Hz, with timestamps ranging from 0.14 to 2.14 seconds after stimulus onset.These CSV files can be easily read using the P andas pac ka ge in Python ( RRID:SCR _ 018214 ).
Additionall y, eac h subject has a corresponding information file named "SUBJECT X.txt."

Figure 1 :
Figure 1: Schematic representation of the single-trial flow of the experiment, divided into 3 phases: cue , stimulate , and rest, lasting 1 second, 2 seconds, and 1 second, r espectiv el y.P anel I outlines the experimental flow for the CA par adigm.Conv ersel y, P anel II provides a common schematic for both the BsV and BV paradigms.

Figure 2 :
Figure 2: Schematic representation of the single-target composition of the dual-frequency SSVEP.Panel I on the left illustrates the single-target composition of the CA paradigm, with a partially zoomed-in schematic showing the alternating frequencies resembling a chessboard grid.Panel I on the right depicts the single-target composition of the BV and BsV paradigms, with a partially zoomed-in view where the difference between the 2 stim ulus fr equencies is not dir ectl y detectable.P anel II details the demodulation pr ocess of the stim ulus tar gets for the BV and BsV par adigms, wher e the fused frequency combinations f 1 and f 2 in the human eye are demodulated by polarized light and displayed to the subject's left and right eyes, r espectiv el y.

Figure 3 :
Figure 3: Schematic representation of the stimulus interface with encoding details.Panel I illustrates the frequency-phase encoding scheme used for the CA and BV paradigms, featuring a total of 40 targets organized into 5 rows and 8 columns.Panel II displays the encoding scheme for the BsV par adigm.Her e, the stim uli on the left side corr espond to those assigned to the left e ye, and those on the right to the right e ye .T he stimuli within dashed boxes indicate the target groups postfrequency swap between the eyes, whereas those within solid boxes represent the original target groups.

Figur e 4 :
Figur e 4: CC A spectr a and normalized PSD topogr a phy for the CA and BV par adigms at fr equencies f 1 of 9 .0 Hz and f 2 of 10 .6 Hz .P anel I: Gr ay lines denote the results from the CA analysis, sourced from CA paradigm group subject 01.Panel II: Blue lines denote the results from the BV paradigm anal ysis, sourced fr om BV par adigm gr oup subject 01.

Figur e 5 :
Figur e 5: CC A spectra of the BsV paradigm with normalized PSD topogr a phy.P anel I: Solid lines r epr esent r esults for a stim ulus tar get with the left eye frequency f 1 of 13 .93 Hz and the right eye frequency f 2 of 10 .9 Hz .Panel II: Dashed lines represent results for stimulus targets with the left eye frequency f 1 of 10 .9 Hz and the right eye frequency f 2 of 13 .93 Hz .All data sourced from BsV paradigm group subject 01.

Figure 6 :
Figure 6: Signal-to-noise ratio distribution for a single trial: gr ay r epr esents the CA par adigm, r ed denotes the BsV paradigm, and blue indicates the BV paradigm.The first column shows the wideband SNR distribution, the second column the narrowband SNR distribution, and the third column the intermodulation SNR distribution.

Figure 7 :
Figure 7: Bar chart of the mean values of wideband SNR, narrowband SNR, and intermodulation SNR: gray corresponds to the CA par adigm, r ed to the BsV paradigm, and blue to the BV paradigm.The asterisks denote the results of W elch' s independent t -tests for significant differences.

Figur e 8 :
Figur e 8: T he plot of untr ained classification r esults ov er time, wher e blue r epr esents the BV par adigm and gr ay r epr esents the CA par adigm.The left gr a ph illustr ates the corr ectness curv e and the right gr a ph displays the ITR curv e. Err or bars indicate standard err ors .T he asterisks denote the results of W elch' s independent t -tests for significant differences.

Figur e 9 :
Figur e 9: T he plot of tr ained classification r esults ov er time, wher e blue indicates the BV par adigm, r ed indicates the BsV par adigm, and gr ay indicates the CA paradigm.The left plot shows the correctness curve and the right plot shows the ITR curve.Error bars are standard errors .T he green asterisks are the results of W elch' s independent t -tests for significant differences between the BV paradigm and the BsV paradigm, and the dark red asterisks are the results of W elch' s independent t -tests for significant differences between the CA paradigm and the BsV paradigm.There was no significant difference between the results of the CA and BV paradigms.

Figure 10 :
Figure 10: Classification accuracy of the 3 paradigms under the TRCA algorithm for different channel configurations .T he left graph represents the CA paradigm, the middle graph represents the BsV paradigm, and the right graph represents the BV paradigm.The channel configurations are 3-channel, 6-c hannel, and 9-c hannel.Results marked with an asterisk indicate significance as determined by W elch' s independent t -test.The asterisks on the top side indicate the results of the statistical test between the 3-channel results and the 9-channel results, and the asterisks on the bottom side indicate the results of the statistical test between the 3-channel results and the 6-channel results .T here is no significant difference between the 6-channel results and the 9-channel results.
First Column: Number Second Column: Timestamp Third Column: Condition (corresponds to the stimulus code in "Stimulate Code.txt")Fourth Column: Epoch number Subsequent Columns : EEG data, with the first row indicating the name of each lead